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The information retrieval community has emphasized the use of test collections and benchmark tasks to measure topical relevance, starting with the Cranfield Experiments of the early 1960s and culminating in the TREC evaluations that continue to this day as the main evaluation framework for information retrieval research.
The IRTF promotes research of importance to the evolution of the Internet by creating focused, long-term research groups working on topics related to Internet protocols, applications, architecture and technology. [1] [2] [3] Unlike the IETF, the task force does not set standards [4] and there is no explicit outcome expected of IRTF research groups.
Relevance is the connection between topics that makes one useful for dealing with the other. Relevance is studied in many different fields, including cognitive science, logic, and library and information science. Epistemology studies it in general, and different theories of knowledge have different implications for what is considered relevant.
Indexing and classification methods to assist with information retrieval have a long history dating back to the earliest libraries and collections however systematic evaluation of their effectiveness began in earnest in the 1950s with the rapid expansion in research production across military, government and education and the introduction of computerised catalogues.
The Networking and Information Technology Research and Development (NITRD) Program (formerly known as High Performance Computing and Communications (HPCC) Program) was created by the High Performance Computing Act of 1991, (P.L. 102-194) [4] and amended by the Next Generation Internet Research Act of 1998 (P.L. 105-305), [5] and the America ...
Information retrieval (IR) in computing and information science is the task of identifying and retrieving information system resources that are relevant to an information need. The information need can be specified in the form of a search query. In the case of document retrieval, queries can be based on full-text or other content-based indexing.
The Computing Research Association (CRA) is a 501(c)3 non-profit association of North American academic departments of computer science, computer engineering, and related fields; laboratories and centers in industry, government, and academia engaging in basic computing research; and affiliated professional societies. [1]
Relevance feedback is a feature of some information retrieval systems. The idea behind relevance feedback is to take the results that are initially returned from a given query, to gather user feedback, and to use information about whether or not those results are relevant to perform a new query.